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Distributed State Estimation Based Distributed Model Predictive Control

Author

Listed:
  • Jing Zeng

    (Liaoning Province Key Laboratory of Control Technology for Chemical Processes, Shenyang University of Chemical Technology, Shenyang 110142, China)

  • Jinfeng Liu

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

Suggested Citation

  • Jing Zeng & Jinfeng Liu, 2021. "Distributed State Estimation Based Distributed Model Predictive Control," Mathematics, MDPI, vol. 9(12), pages 1-16, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1327-:d:571551
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    Cited by:

    1. Hengzhan Yang & Dian Xi & Xu Weng & Fucai Qian & Bo Tan, 2022. "A Numerical Algorithm for Self-Learning Model Predictive Control in Servo Systems," Mathematics, MDPI, vol. 10(17), pages 1-16, September.

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